In this tutorial, classification using Weka Explorer is demonstrated. This is the very basic tutorial where a simple classifier is applied on a dataset in a 10 Fold CV. For more variations of classification, watch out other tutorials on this channel.

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In this tutorial, I showed how to interact with the Weka API for the first time with a simple Java code. In this code, I have loaded an ARFF file called 2.arff and then used Naive Bayes classifier with a 10 fold CV setup. I showed the standard output of Weka on the Eclipse output as well as the F-score, precision and recall of the 10 fold CV.

In this tutorial it is described how to train a J48 decision tree classifier to classify certain sentences into three different classes. Afterwords we save this classification model in order to use it for a different testing set of sentences. While doing so, the most important informations displayed in the plaintext output are explained.
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.

In this tutorial, you will learn how to use Weka Experimenter to compare the performances of multiple classifiers on single or multiple datasets. Please subscribe to get more updates and like if the tutorial is useful.
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Hi Everyone,
We usually create a data model but we restrict ourselves till the model creation but we actually don't predict the future values.
This video is about how you can predict the target variable values in decision tree. I have used Weka for this implementation.
The data set i have used is "Vote" dataset which comes along with Weka. I create 2 data sets - one was Training data set without last 30 rows, and Test data set with last 30 rows but no values for target variable. You can create test data set with "?" implanted for target values in test set.